from PIL import Image
from torchvision import transforms

import gradio as gr
import requests
import torch


model = torch.hub.load('pytorch/vision:v0.6.0', 'resnet18', pretrained=True).eval()
print(model)


# Download human-readable labels for ImageNet.
response = requests.get("https://git.io/JJkYN")
labels = response.text.split("\n")


def predict(inp):
    inp = Image.fromarray(inp.astype('uint8'), 'RGB')
    inp = transforms.ToTensor()(inp).unsqueeze(0)
    print('inp.shape', inp.shape)

    with torch.no_grad():
        prediction = torch.nn.functional.softmax(model(inp)[0], dim=0)
        print('prediction', prediction)
    
    return {labels[i]: float(prediction[i]) for i in range(1000) }

if __name__ == '__main__':
    inputs = gr.inputs.Image()
    outputs = gr.outputs.Label(num_top_classes=5)
    interface = gr.Interface(fn=predict, inputs=inputs, outputs=outputs,
        examples=['cat.jpg', 'dog.jpg'])
    interface.launch(share=True)
